{
 "cells": [
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [
    {
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      "('iteration', 49600, ':l2_error', array([ 1.65820706], dtype=float32))\n",
      "('iteration', 49700, ':l2_error', array([ 1.61146116], dtype=float32))\n",
      "('iteration', 49800, ':l2_error', array([ 1.69105804], dtype=float32))\n",
      "('iteration', 49900, ':l2_error', array([ 1.7399708], dtype=float32))\n",
      "('iteration', 50000, ':l2_error', array([ 1.83616698], dtype=float32))\n",
      "('iteration', 50100, ':l2_error', array([ 1.82161987], dtype=float32))\n",
      "('iteration', 50200, ':l2_error', array([ 1.65204895], dtype=float32))\n",
      "('iteration', 50300, ':l2_error', array([ 1.60843945], dtype=float32))\n",
      "('iteration', 50400, ':l2_error', array([ 1.6881572], dtype=float32))\n",
      "('iteration', 50500, ':l2_error', array([ 1.73499715], dtype=float32))\n",
      "('iteration', 50600, ':l2_error', array([ 1.82672465], dtype=float32))\n",
      "('iteration', 50700, ':l2_error', array([ 1.81448925], dtype=float32))\n",
      "('iteration', 50800, ':l2_error', array([ 1.64766479], dtype=float32))\n",
      "('iteration', 50900, ':l2_error', array([ 1.60190189], dtype=float32))\n",
      "('iteration', 51000, ':l2_error', array([ 1.68309665], dtype=float32))\n",
      "('iteration', 51100, ':l2_error', array([ 1.74678409], dtype=float32))\n",
      "('iteration', 51200, ':l2_error', array([ 1.83002996], dtype=float32))\n",
      "('iteration', 51300, ':l2_error', array([ 1.81412923], dtype=float32))\n",
      "('iteration', 51400, ':l2_error', array([ 1.63803363], dtype=float32))\n",
      "('iteration', 51500, ':l2_error', array([ 1.60204697], dtype=float32))\n",
      "('iteration', 51600, ':l2_error', array([ 1.67659259], dtype=float32))\n",
      "('iteration', 51700, ':l2_error', array([ 1.73306382], dtype=float32))\n",
      "('iteration', 51800, ':l2_error', array([ 1.82546711], dtype=float32))\n",
      "('iteration', 51900, ':l2_error', array([ 1.80210447], dtype=float32))\n",
      "('iteration', 52000, ':l2_error', array([ 1.64077055], dtype=float32))\n",
      "('iteration', 52100, ':l2_error', array([ 1.59640121], dtype=float32))\n",
      "('iteration', 52200, ':l2_error', array([ 1.66743267], dtype=float32))\n",
      "('iteration', 52300, ':l2_error', array([ 1.72822607], dtype=float32))\n",
      "('iteration', 52400, ':l2_error', array([ 1.82413411], dtype=float32))\n",
      "('iteration', 52500, ':l2_error', array([ 1.79275084], dtype=float32))\n",
      "('iteration', 52600, ':l2_error', array([ 1.64195812], dtype=float32))\n",
      "('iteration', 52700, ':l2_error', array([ 1.58986175], dtype=float32))\n",
      "('iteration', 52800, ':l2_error', array([ 1.66964293], dtype=float32))\n",
      "('iteration', 52900, ':l2_error', array([ 1.72460604], dtype=float32))\n",
      "('iteration', 53000, ':l2_error', array([ 1.82442057], dtype=float32))\n",
      "('iteration', 53100, ':l2_error', array([ 1.79696774], dtype=float32))\n",
      "('iteration', 53200, ':l2_error', array([ 1.63409412], dtype=float32))\n",
      "('iteration', 53300, ':l2_error', array([ 1.58411884], dtype=float32))\n",
      "('iteration', 53400, ':l2_error', array([ 1.66773021], dtype=float32))\n",
      "('iteration', 53500, ':l2_error', array([ 1.72035718], dtype=float32))\n",
      "('iteration', 53600, ':l2_error', array([ 1.82623124], dtype=float32))\n",
      "('iteration', 53700, ':l2_error', array([ 1.79645443], dtype=float32))\n",
      "('iteration', 53800, ':l2_error', array([ 1.63436246], dtype=float32))\n",
      "('iteration', 53900, ':l2_error', array([ 1.58191633], dtype=float32))\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.insert(0,'/home/csunix/schtmt/NewFolder/caffe_Sep/python')\n",
    "sys.path.insert(0,'/home/csunix/schtmt/NewFolder/caffe_Sep/examples/mnist_wta_autoencoder')\n",
    "import numpy as np\n",
    "# import matplotlib.pyplot as plt\n",
    "import caffe\n",
    "caffe.set_device(0)\n",
    "caffe.set_mode_gpu()\n",
    "solver = caffe.SGDSolver('mnist_wta_leakyReLU_solver.prototxt')\n",
    "# # #solver.net.params['lstm1'][2].data[15:30]=5\n",
    "# [(k, v.data.shape) for k, v in solver.net.blobs.items()]\n",
    "# # just print the weight sizes (we'll omit the biases)\n",
    "# [(k, v[0].data.shape) for k, v in solver.net.params.items()]\n",
    "#\n",
    "# #solver.net.forward()\n",
    "#\n",
    "niter = 100000\n",
    "train_loss = np.zeros(niter)\n",
    "\n",
    "for i in range(niter):\n",
    "    solver.step(1)\n",
    "#     print solver.net.blobs.keys()\n",
    "#     #train_loss[i] = solver.net.blobs['loss'].data    \n",
    "    loss = solver.net.blobs['l2_error'].data\n",
    "#     print('iteration', i, ':l2_error', loss) \n",
    "    if (i%100 == 0):\n",
    "        print('iteration', i, ':l2_error', loss) \n",
    "    train_loss[i] = loss\n",
    "    np.save('train_loss_leakyReLU_Sep2017',train_loss)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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